1 Introduction

Here, we will perform patient- and time-point-specific clustering. If we can indeed predict Richter-like cells at the early stages of the disease, then they should cluster together without having the Richter time-point present. On the other hand, if they were poor-quality cells, then they might scatter everywhere in the absence of a Richter-like cluster.

2 Pre-processing

2.1 Load packages

library(Seurat)
library(tidyverse)

2.2 Define parameters

# Paths
path_to_12 <- here::here("results/R_objects/6.seurat_annotated_12.rds")
path_to_19 <- here::here("results/R_objects/6.seurat_annotated_19.rds")
path_to_63 <- here::here("results/R_objects/patient_63/3.seurat_annotated.rds")
path_to_365 <- here::here("results/R_objects/6.seurat_annotated_365.rds")
path_to_3299 <- here::here("results/R_objects/6.seurat_annotated_3299.rds")
path_to_save <- here::here("results/R_objects/7.seurat_time_point_specific_list.rds")


# Source functions
source(here::here("bin/utils.R"))

2.3 Load data

paths_to_load <- c(
  "12" = path_to_12,
  "19" = path_to_19,
  "63" = path_to_63,
  "365" = path_to_365,
  "3299" = path_to_3299
)
seurat_list <- purrr::map(paths_to_load, readRDS)
seurat_list
## $`12`
## An object of class Seurat 
## 23326 features across 5785 samples within 1 assay 
## Active assay: RNA (23326 features, 2000 variable features)
##  3 dimensional reductions calculated: pca, harmony, umap
## 
## $`19`
## An object of class Seurat 
## 23326 features across 7284 samples within 1 assay 
## Active assay: RNA (23326 features, 2000 variable features)
##  3 dimensional reductions calculated: pca, harmony, umap
## 
## $`63`
## An object of class Seurat 
## 13680 features across 983 samples within 1 assay 
## Active assay: RNA (13680 features, 2000 variable features)
##  2 dimensional reductions calculated: pca, umap
## 
## $`365`
## An object of class Seurat 
## 23326 features across 4685 samples within 1 assay 
## Active assay: RNA (23326 features, 2000 variable features)
##  3 dimensional reductions calculated: pca, harmony, umap
## 
## $`3299`
## An object of class Seurat 
## 23326 features across 6063 samples within 1 assay 
## Active assay: RNA (23326 features, 2000 variable features)
##  3 dimensional reductions calculated: pca, harmony, umap
umaps_annotations <- purrr::map(names(seurat_list), function(x) {
  p <- plot_annotation(
    seurat_list[[x]],
    pt_size = 0.5,
    colors_reference = color_annotations,
    patient_id = x
  )
  p +
    ggtitle(x) +
    theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5))
})
umaps_annotations
## [[1]]

## 
## [[2]]

## 
## [[3]]

## 
## [[4]]

## 
## [[5]]

3 Time point-specific dimensionality reduction

seurat_list_list <- purrr::map(seurat_list, function(seurat_obj) {
  seurat_obj$time_point2 <- str_c(
    seurat_obj$time_point,
    seurat_obj$tissue,
    sep = "_"
  )
  seurat_obj_list <- SplitObject(seurat_obj, split.by = "time_point2")
  seurat_obj_list <- seurat_obj_list[sort(unique(seurat_obj$time_point2))]
  seurat_obj_list <- purrr::map(seurat_obj_list, process_seurat, dims = 1:20)
  seurat_obj_list
})

4 Plot

umaps_time_points <- purrr::map2(seurat_list_list, names(seurat_list_list), function(seurat_obj_list, patient_id) {
  purrr::map2(seurat_obj_list, names(seurat_obj_list), function(seurat_obj, x) {
    p <- plot_annotation(
      seurat_obj = seurat_obj,
      pt_size = 0.65,
      colors_reference = color_annotations,
      patient_id = patient_id,
      nothing = TRUE
    )
    p +
      ggtitle(x) +
      theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5))
  })
})

print("Case 12")
## [1] "Case 12"
umaps_time_points$`12`
## $T1_PB

## 
## $T2_PB

## 
## $T4_PB

## 
## $T5_PB

## 
## $T6_PB

print("Case 19")
## [1] "Case 19"
umaps_time_points$`19`
## $T1_PB

## 
## $T3_PB

## 
## $T4_PB

## 
## $T5_PB

## 
## $T6_PB

print("Case 63")
## [1] "Case 63"
umaps_time_points$`63`
## $T1_LN

## 
## $T1_PB

## 
## $T2_PB

## 
## $T3_LN

print("Case 365")
## [1] "Case 365"
umaps_time_points$`365`
## $T2_PB

## 
## $T3_LN

print("Case 3299")
## [1] "Case 3299"
umaps_time_points$`3299`
## $T1_BM

## 
## $T2_BM

## 
## $T3_BM

5 Save

saveRDS(seurat_list_list, path_to_save)

6 Session Information

sessionInfo()
## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=es_ES.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=es_ES.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1      stringr_1.4.0      dplyr_1.0.7        purrr_0.3.4        readr_1.4.0        tidyr_1.1.3        tibble_3.1.3       ggplot2_3.3.5      tidyverse_1.3.1    SeuratObject_4.0.2 Seurat_4.0.3       BiocStyle_2.18.1  
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1          backports_1.2.1       plyr_1.8.6            igraph_1.2.6          lazyeval_0.2.2        splines_4.0.4         listenv_0.8.0         scattermore_0.7       digest_0.6.27         htmltools_0.5.1.1     fansi_0.5.0           magrittr_2.0.1        tensor_1.5            cluster_2.1.1         ROCR_1.0-11           globals_0.14.0        modelr_0.1.8          matrixStats_0.59.0    spatstat.sparse_2.0-0 colorspace_2.0-2      rvest_1.0.0           ggrepel_0.9.1         haven_2.4.1           xfun_0.23             crayon_1.4.1          jsonlite_1.7.2        spatstat.data_2.1-0   survival_3.2-10       zoo_1.8-9             glue_1.4.2            polyclip_1.10-0       gtable_0.3.0          leiden_0.3.8          future.apply_1.7.0    abind_1.4-5           scales_1.1.1          DBI_1.1.1             miniUI_0.1.1.1        Rcpp_1.0.7            viridisLite_0.4.0     xtable_1.8-4          reticulate_1.20       spatstat.core_2.1-2   htmlwidgets_1.5.3     httr_1.4.2            RColorBrewer_1.1-2    ellipsis_0.3.2        ica_1.0-2             pkgconfig_2.0.3       farver_2.1.0          sass_0.4.0            uwot_0.1.10           dbplyr_2.1.1          deldir_0.2-10        
##  [55] utf8_1.2.2            here_1.0.1            tidyselect_1.1.1      labeling_0.4.2        rlang_0.4.11          reshape2_1.4.4        later_1.2.0           munsell_0.5.0         cellranger_1.1.0      tools_4.0.4           cli_3.0.1             generics_0.1.0        broom_0.7.7           ggridges_0.5.3        evaluate_0.14         fastmap_1.1.0         yaml_2.2.1            goftest_1.2-2         knitr_1.33            fs_1.5.0              fitdistrplus_1.1-5    RANN_2.6.1            pbapply_1.4-3         future_1.21.0         nlme_3.1-152          mime_0.10             xml2_1.3.2            compiler_4.0.4        rstudioapi_0.13       plotly_4.9.4          png_0.1-7             spatstat.utils_2.2-0  reprex_2.0.0          bslib_0.2.5.1         stringi_1.6.2         highr_0.9             RSpectra_0.16-0       lattice_0.20-41       Matrix_1.3-4          vctrs_0.3.8           pillar_1.6.2          lifecycle_1.0.0       BiocManager_1.30.15   spatstat.geom_2.1-0   lmtest_0.9-38         jquerylib_0.1.4       RcppAnnoy_0.0.19      data.table_1.14.0     cowplot_1.1.1         irlba_2.3.3           httpuv_1.6.1          patchwork_1.1.1       R6_2.5.0              bookdown_0.22        
## [109] promises_1.2.0.1      KernSmooth_2.23-18    gridExtra_2.3         parallelly_1.26.0     codetools_0.2-18      MASS_7.3-53.1         assertthat_0.2.1      rprojroot_2.0.2       withr_2.4.2           sctransform_0.3.2     mgcv_1.8-36           parallel_4.0.4        hms_1.1.0             grid_4.0.4            rpart_4.1-15          rmarkdown_2.8         Rtsne_0.15            shiny_1.6.0           lubridate_1.7.10